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The IBM Developer UnConference is an exchange, meeting and and learning platform for developers, IT architects, data scientists and for all those who are interested in facts & trends of application development, cloud, data science, analytics, artificial intelligence and more.

This event is free-of-charge. The apéro is sponsored by Avnet/Techdata. Lunch is currently not sponsored.

Pls don't book more than one ticket per person (of course per event). We need to print personal badges and grant personal Wifi access. Thanks!

Many software development organizations strive to enhance the productivity of their developers. All too often, efforts aimed at improving developer productivity are undertaken without knowledge about how developers spend their time at work and how it influences their own perception of productivity. To fill in this gap, we deployed a monitoring application at 20 computers of professional software developers from four companies for an average of 11 full workdays in situ. Corroborating earlier findings, we found that developers spend their time on a wide variety of activities and switch regularly between them, resulting in highly fragmented work. Our findings extend beyond existing research in that we correlate developers’ work habits with perceived productivity and also show productivity is a personal matter. Although productivity is personal, developers can be roughly grouped into morning, low-at-lunch and afternoon people. A stepwise linear regression per participant revealed that more user input is most often associated with a positive, and emails, planned meetings and work unrelated websites with a negative perception of productivity. We discuss opportunities of our findings, the potential to predict high and low productivity and suggest design approaches to create better tool support for planning developers’ workdays and improving their personal productivity.

Realtime- Cognitive IoT using DeepLearning and Online Learning on top of ApacheSpark Streaming and Spark enabled DL frameworks like DeepLearning4J, ApacheSystemML and TensorFlow in SparkDeepLearning frameworks are popping up at very high frequency but only a few of them are suitable to run on clusters, use GPUs and supporting topologies beyond Feed-Forward at the same time. DeepLearning4J, ApacheSystemML and TensorSpark feature all this without forcing you to learn new exotic programming languages and in addition also scales-out on well established infrastructures like ApacheSpark. In this talk we will introduce DeepLearning4J on top of ApacheSpark with an example to create an anomaly detector for IoT sensor data with a LSTM auto encoder neural network. We’ll also explain how ApacheSystemML uses cost-based optimisers for Neural Network training and how TensorSpark parallelises TensorFlow on ApacheSpark.

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